Update.

diff --git a/main.py b/main.py
index 74e70b2..1a17e51 100755 (executable)
--- a/main.py
+++ b/main.py
@@ -66,6 +66,16 @@ parser.add_argument("--learning_rate", type=float, default=6e-4)
 
 parser.add_argument("--min_learning_rate", type=float, default=6e-5)
 
+# legacy
+
+parser.add_argument("--legacy_lr_schedule", action="store_true", default=False)
+
+parser.add_argument("--legacy_learning_rate", type=float, default=1e-4)
+
+parser.add_argument("--legacy_min_learning_rate", type=float, default=2e-5)
+
+parser.add_argument("--nb_large_lr_epochs", type=float, default=10)
+
 ########################################
 
 parser.add_argument("--model", type=str, default=None)
@@ -460,10 +470,21 @@ for n in vars(args):
 
 ######################################################################
 
-# from nanoGPT
 
+def get_lr(n_epoch, it):
+    if args.legacy_lr_schedule:
+        # my crude scheduling to compare to previous baseline, added
+        # warmup though
+
+        if it < args.nb_warmup_iter:
+            return args.legacy_learning_rate * it / args.nb_warmup_iter
+        elif it < args.nb_large_lr_epochs:
+            return args.legacy_learning_rate
+        else:
+            return args.legacy_min_learning_rate
+
+    # from nanoGPT
 
-def get_lr(it):
     # 1) linear warmup for warmup_iter steps
     if it < args.nb_warmup_iter:
         return args.learning_rate * it / args.nb_warmup_iter
@@ -848,7 +869,7 @@ for n_epoch in range(nb_epochs_finished, nb_epochs):
         total_loss = loss + (args.rho * inner_loss if args.rho > 0 else 0.0)
 
         it += 1
-        lr = get_lr(it)
+        lr = get_lr(n_epoch, it)
         for param_group in optimizer.param_groups:
             param_group["lr"] = lr
 

diff --git a/tasks.py b/tasks.py
index 58638ed..afad8af 100755 (executable)
--- a/tasks.py
+++ b/tasks.py
@@ -58,7 +58,7 @@ def masked_inplace_autoregression(
 
 
 class Task:
-    def batches(self, split="train"):
+    def batches(self, split="train", desc=None):
         pass
 
     def vocabulary_size(self):
@@ -328,7 +328,7 @@ class PicoCLVR(Task):
         self.train_input = self.tensorize(self.train_descr)
         self.test_input = self.tensorize(self.test_descr)
 
-    def batches(self, split="train"):
+    def batches(self, split="train", desc=None):
         assert split in {"train", "test"}
         input = self.train_input if split == "train" else self.test_input
         for batch in tqdm.tqdm(